@article {70285, title = {Whole-Brain Co-Mapping of Gene Expression and Neuronal Activity at Cellular Resolution in Behaving Zebrafish}, journal = {bioRxiv}, year = {2026}, month = {2026 Feb 10}, abstract = {

The brain{\textquoteright}s capabilities rely on both the molecular properties of individual cells and their interactions across brain-wide networks. However, relating gene expression to activity in individual neurons across the entire brain remains elusive. Here we developed an experimental-computational platform, WARP, for whole-brain imaging of neuronal activity during behavior, expansion-assisted spatial transcriptomics, and cellular-level registration of these two modalities. Through joint analysis of whole-brain neuronal activity during multiple behaviors, cellular gene expression, and anatomy, we identified functions of molecularly defined populations {\textemdash} including luminance coding in a cckb-pou4f2 midbrain population and task-structured activity in pvalb7-eomesa hippocampal-like neurons {\textemdash} and defined over 2,000 other function-gene-anatomy subpopulations. Analysis of this unprecedented multimodal dataset also revealed that most gene-matched neurons showed stronger activity correlations, highlighting a brain-wide role for gene expression in functional organization. WARP establishes a foundational platform and open-access dataset for cross-experiment discovery, high-throughput function-to-gene mapping, unification of cell biology and systems neuroscience, and scalable circuit modeling at the whole-brain scale.

}, doi = {10.64898/2026.02.07.704095}, url = {https://www.biorxiv.org/content/early/2026/02/10/2026.02.07.704095}, author = {Marquez Legorreta, Emmanuel and Fleishman, Greg M. and Hesselink, Luuk W. and Eddison, Mark and Smeets, Kasper and Stringer, Carsen and Keller, Philipp J. and Narayan, Sujatha and Chen, Alex B. and Mensh, Brett D. and Sternson, Scott M. and Englitz, Bernhard and Tillberg, Paul W. and Ahrens, Misha B.} } @article {70224, title = {SpotDMix: informed mRNA transcript assignment using mixture models}, journal = {bioRxiv}, year = {2025}, month = {2025 Dec 18}, abstract = {

Unveiling the genetic profiles of spatially distinguished cells is an important aspect in many areas of brain research, as the genetic identity contains information about a cell{\textquoteright}s physiological properties and internal state. On top of this, knowledge of the genetic details of each cell can reveal structural organization within tissue. As image-based spatial transcriptomics moves toward applications in tissues with dense cellular packing, accurate assignment of detected mRNA transcripts ("spots") to correct segmented cells becomes increasingly difficult, rendering simple methods insufficient with many incorrect assignments to neighboring cells. Here we introduce SpotDMix, a statistical model for assigning spots to cells by modeling spots as coming from a mixture model of distributions matching segmented cell shapes, with assignment probabilities and shape parameters optimized using the Expectation Maximization algorithm. Performance is assessed and compared against several simple methods in various scenarios on both surrogate data and larval zebrafish data. In all tested scenarios SpotDMix outperforms the simple methods on all evaluated metrics, including individual transcript assignment accuracy, total assigned number of spots per cell error and cell type classification. Further, SpotDMix produces a higher degree of exclusivity between genes which are known to not or rarely co-express.

}, doi = {10.64898/2025.12.15.693918}, url = {https://www.biorxiv.org/content/early/2025/12/18/2025.12.15.693918}, author = {Smeets, Kasper and Hesselink, Luuk W and Marquez-Legorreta, Emmanuel and Fleishman, Greg M and Eddison, Mark and Tillberg, Paul W and Ahrens, Misha B and Englitz, Bernhard} }